import collections
import json
import os

import datasets


_HOMEPAGE = "https://universe.roboflow.com/mohamed-traore-2ekkp/gtsdb---german-traffic-sign-detection-benchmark/dataset/1"
_LICENSE = "CC BY 4.0"
_CITATION = """\
@misc{ gtsdb---german-traffic-sign-detection-benchmark_dataset,
    title = { GTSDB - German Traffic Sign Detection Benchmark Dataset },
    type = { Open Source Dataset },
    author = { Mohamed Traore },
    howpublished = { \\url{ https://universe.roboflow.com/mohamed-traore-2ekkp/gtsdb---german-traffic-sign-detection-benchmark } },
    url = { https://universe.roboflow.com/mohamed-traore-2ekkp/gtsdb---german-traffic-sign-detection-benchmark },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { jul },
    note = { visited on 2023-01-16 },
}
"""
_CATEGORIES = ['animals', 'construction', 'cycles crossing', 'danger', 'no entry', 'pedestrian crossing', 'school crossing', 'snow', 'stop', 'bend', 'bend left', 'bend right', 'give way', 'go left', 'go left or straight', 'go right', 'go right or straight', 'go straight', 'keep left', 'keep right', 'no overtaking', 'no overtaking -trucks-', 'no traffic both ways', 'no trucks', 'priority at next intersection', 'priority road', 'restriction ends', 'restriction ends -overtaking -trucks--', 'restriction ends -overtaking-', 'restriction ends 80', 'road narrows', 'roundabout', 'slippery road', 'speed limit 100', 'speed limit 120', 'speed limit 20', 'speed limit 30', 'speed limit 50', 'speed limit 60', 'speed limit 70', 'speed limit 80', 'traffic signal', 'uneven road']
_ANNOTATION_FILENAME = "_annotations.coco.json"


class GERMANTRAFFICSIGNDETECTIONConfig(datasets.BuilderConfig):
    """Builder Config for german-traffic-sign-detection"""

    def __init__(self, data_urls, **kwargs):
        """
        BuilderConfig for german-traffic-sign-detection.

        Args:
          data_urls: `dict`, name to url to download the zip file from.
          **kwargs: keyword arguments forwarded to super.
        """
        super(GERMANTRAFFICSIGNDETECTIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.data_urls = data_urls


class GERMANTRAFFICSIGNDETECTION(datasets.GeneratorBasedBuilder):
    """german-traffic-sign-detection object detection dataset"""

    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        GERMANTRAFFICSIGNDETECTIONConfig(
            name="full",
            description="Full version of german-traffic-sign-detection dataset.",
            data_urls={
                "train": "https://huggingface.co/datasets/keremberke/german-traffic-sign-detection/resolve/main/data/train.zip",
                "validation": "https://huggingface.co/datasets/keremberke/german-traffic-sign-detection/resolve/main/data/valid.zip",
                "test": "https://huggingface.co/datasets/keremberke/german-traffic-sign-detection/resolve/main/data/test.zip",
            },
        ),
        GERMANTRAFFICSIGNDETECTIONConfig(
            name="mini",
            description="Mini version of german-traffic-sign-detection dataset.",
            data_urls={
                "train": "https://huggingface.co/datasets/keremberke/german-traffic-sign-detection/resolve/main/data/valid-mini.zip",
                "validation": "https://huggingface.co/datasets/keremberke/german-traffic-sign-detection/resolve/main/data/valid-mini.zip",
                "test": "https://huggingface.co/datasets/keremberke/german-traffic-sign-detection/resolve/main/data/valid-mini.zip",
            },
        )
    ]

    def _info(self):
        features = datasets.Features(
            {
                "image_id": datasets.Value("int64"),
                "image": datasets.Image(),
                "width": datasets.Value("int32"),
                "height": datasets.Value("int32"),
                "objects": datasets.Sequence(
                    {
                        "id": datasets.Value("int64"),
                        "area": datasets.Value("int64"),
                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                        "category": datasets.ClassLabel(names=_CATEGORIES),
                    }
                ),
            }
        )
        return datasets.DatasetInfo(
            features=features,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        data_files = dl_manager.download_and_extract(self.config.data_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "folder_dir": data_files["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "folder_dir": data_files["validation"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "folder_dir": data_files["test"],
                },
            ),
]

    def _generate_examples(self, folder_dir):
        def process_annot(annot, category_id_to_category):
            return {
                "id": annot["id"],
                "area": annot["area"],
                "bbox": annot["bbox"],
                "category": category_id_to_category[annot["category_id"]],
            }

        image_id_to_image = {}
        idx = 0

        annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
        with open(annotation_filepath, "r") as f:
            annotations = json.load(f)
        category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
        image_id_to_annotations = collections.defaultdict(list)
        for annot in annotations["annotations"]:
            image_id_to_annotations[annot["image_id"]].append(annot)
        filename_to_image = {image["file_name"]: image for image in annotations["images"]}

        for filename in os.listdir(folder_dir):
            filepath = os.path.join(folder_dir, filename)
            if filename in filename_to_image:
                image = filename_to_image[filename]
                objects = [
                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
                ]
                with open(filepath, "rb") as f:
                    image_bytes = f.read()
                yield idx, {
                    "image_id": image["id"],
                    "image": {"path": filepath, "bytes": image_bytes},
                    "width": image["width"],
                    "height": image["height"],
                    "objects": objects,
                }
                idx += 1